RT Journal Article
SR Electronic
T1 Narrow-sense heritability estimation of complex traits using identity-by-descent information
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 164848
DO 10.1101/164848
A1 Luke M. Evans
A1 Rasool Tahmasbi
A1 Matthew Jones
A1 Scott I. Vrieze
A1 Gonçalo R. Abecasis
A1 Sayantan Das
A1 Doug W. Bjelland
A1 Teresa R. deCandia
A1 - Haplotype Reference Consortium
A1 Gonçalo Abecasis
A1 David Altshuler
A1 Carl A Anderson
A1 Andrea Angius
A1 Jeffrey C Barrett
A1 Sonja Berndt
A1 Michael Boehnke
A1 Dorrett Boomsma
A1 Kari Branham
A1 Gerome Breen
A1 Chad M Brummett
A1 Fabio Busonero
A1 Harry Campbell
A1 Peter Campbell
A1 Andrew Chan
A1 Sai Chen
A1 Emily Chew
A1 Massimiliano Cocca
A1 Francis S Collins
A1 Laura J Corbin
A1 Francesco Cucca
A1 Petr Danecek
A1 Sayantan Das
A1 Paul I W de Bakker
A1 George Dedoussis
A1 Annelot Dekker
A1 Olivier Delaneau
A1 Marcus Dorr
A1 Richard Durbin
A1 Aliki-Eleni Farmaki
A1 Luigi Ferrucci
A1 Lukas Forer
A1 Ross M Fraser
A1 Timothy Frayling
A1 Christian Fuchsberger
A1 Stacey Gabriel
A1 Ilaria Gandin
A1 Paolo Gasparini
A1 Christopher E Gillies
A1 Arthur Gilly
A1 Leif Groop
A1 Tabitha Harrison
A1 Andrew Hattersley
A1 Oddgeir L Holmen
A1 Kristian Hveem
A1 William Iacono
A1 Amit Joshi
A1 Hyun Min Kang
A1 Hamed Khalili
A1 Charles Kooperberg
A1 Seppo Koskinen
A1 Matthias Kretzler
A1 Warren Kretzschmar
A1 Alan Kwong
A1 James C Lee
A1 Shawn Levy
A1 Yang Luo
A1 Anubha Mahajan
A1 Jonathan Marchini
A1 Steven McCarroll
A1 Mark I McCarthy
A1 Shane McCarthy
A1 Matt McGue
A1 Melvin McInnis
A1 Thomas Meitinger
A1 David Melzer
A1 Massimo Mezzavilla
A1 Josine L Min
A1 Karen L Mohlke
A1 Richard M Myers
A1 Matthias Nauck
A1 Deborah Nickerson
A1 Aarno Palotie
A1 Carlos Pato
A1 Michele Pato
A1 Ulrike Peters
A1 Nicola Pirastu
A1 Wouter Van Rheenen
A1 J Brent Richards
A1 Samuli Ripatti
A1 Cinzia Sala
A1 Veikko Salomaa
A1 Matthew G Sampson
A1 David Schlessinger
A1 Robert E Schoen
A1 Sebastian Schoenherr
A1 Laura J Scott
A1 Kevin Sharp
A1 Carlo Sidore
A1 P Eline Slagboom
A1 Kerrin Small
A1 George Davey Smith
A1 Nicole Soranzo
A1 Timothy Spector
A1 Dwight Stambolian
A1 Anand Swaroop
A1 Morris A Swertz
A1 Alexander Teumer
A1 Nicholas Timpson
A1 Daniela Toniolo
A1 Michela Traglia
A1 Marcus Tuke
A1 Jaakko Tuomilehto
A1 Leonard H Van den Berg
A1 Cornelia M van Duijn
A1 Jan Veldink
A1 John B Vincent
A1 Uwe Volker
A1 Scott Vrieze
A1 Klaudia Walter
A1 Cisca Wijmenga
A1 Cristen Willer
A1 James F Wilson
A1 Andrew R Wood
A1 Eleftheria Zeggini
A1 He Zhang
A1 Jian Yang
A1 Michael E. Goddard
A1 Peter M. Visscher
A1 Matthew C. Keller
YR 2017
UL http://biorxiv.org/content/early/2017/07/17/164848.abstract
AB Heritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly-tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). IBD-based heritability estimates were unbiased when using unrelated individuals, even for traits with extremely rare CVs, but stratification led to strong biases in IBD-based heritability estimates with poor precision. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied successfully to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in this real data that were not predicted from our simulation, suggesting that more work may be required to understand factors that influence IBD-based estimates.